7 citations · 10 across the 5 of their papers we have counts for
5 papers
FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models
Jingwei Sun, Ziyue Xu, Hongxu Yin +4
Pre-trained language models (PLM) have revolutionized the NLP landscape, achieving stellar performances across diverse tasks. These models, while benefiting from vast training data…
PrivaScissors: Enhance the Privacy of Collaborative Inference through the Lens of Mutual Information
Lin Duan, Jingwei Sun, Yiran Chen +1
Edge-cloud collaborative inference empowers resource-limited IoT devices to support deep learning applications without disclosing their raw data to the cloud server, thus preservin…
Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples
Jingwei Sun, Ziyue Xu, Dong Yang +6
Federated learning is a popular collaborative learning approach that enables clients to train a global model without sharing their local data. Vertical federated learning (VFL) dea…
Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Parties
Jingwei Sun, Zhixu Du, Anna Dai +4
Vertical federated learning (VFL) enables a service provider (i.e., active party) who owns labeled features to collaborate with passive parties who possess auxiliary features to im…
AdaSAM: Boosting Sharpness-Aware Minimization with Adaptive Learning Rate and Momentum for Training Deep Neural Networks
Hao Sun, Li Shen, Qihuang Zhong +6
Sharpness aware minimization (SAM) optimizer has been extensively explored as it can generalize better for training deep neural networks via introducing extra perturbation steps to…